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| # coding=utf-8 | |
| # Copyright 2019 HuggingFace Inc. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import copy | |
| import logging | |
| import os.path | |
| import random | |
| import tempfile | |
| import unittest | |
| from transformers import is_torch_available | |
| from .utils import require_torch, slow, torch_device | |
| if is_torch_available(): | |
| import torch | |
| import numpy as np | |
| from transformers import ( | |
| AdaptiveEmbedding, | |
| PretrainedConfig, | |
| PreTrainedModel, | |
| BertModel, | |
| BertConfig, | |
| BERT_PRETRAINED_MODEL_ARCHIVE_MAP, | |
| ) | |
| def _config_zero_init(config): | |
| configs_no_init = copy.deepcopy(config) | |
| for key in configs_no_init.__dict__.keys(): | |
| if "_range" in key or "_std" in key or "initializer_factor" in key: | |
| setattr(configs_no_init, key, 0.0) | |
| return configs_no_init | |
| class ModelTesterMixin: | |
| model_tester = None | |
| all_model_classes = () | |
| test_torchscript = True | |
| test_pruning = True | |
| test_resize_embeddings = True | |
| test_head_masking = True | |
| is_encoder_decoder = False | |
| def test_save_load(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| for model_class in self.all_model_classes: | |
| model = model_class(config) | |
| model.to(torch_device) | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| out_2 = outputs[0].numpy() | |
| out_2[np.isnan(out_2)] = 0 | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| model.save_pretrained(tmpdirname) | |
| model = model_class.from_pretrained(tmpdirname) | |
| model.to(torch_device) | |
| with torch.no_grad(): | |
| after_outputs = model(**inputs_dict) | |
| # Make sure we don't have nans | |
| out_1 = after_outputs[0].cpu().numpy() | |
| out_1[np.isnan(out_1)] = 0 | |
| max_diff = np.amax(np.abs(out_1 - out_2)) | |
| self.assertLessEqual(max_diff, 1e-5) | |
| def test_initialization(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| configs_no_init = _config_zero_init(config) | |
| for model_class in self.all_model_classes: | |
| model = model_class(config=configs_no_init) | |
| for name, param in model.named_parameters(): | |
| if param.requires_grad: | |
| self.assertIn( | |
| param.data.mean().item(), | |
| [0.0, 1.0], | |
| msg="Parameter {} of model {} seems not properly initialized".format(name, model_class), | |
| ) | |
| def test_determinism(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| for model_class in self.all_model_classes: | |
| model = model_class(config) | |
| model.to(torch_device) | |
| model.eval() | |
| with torch.no_grad(): | |
| first = model(**inputs_dict)[0] | |
| second = model(**inputs_dict)[0] | |
| out_1 = first.cpu().numpy() | |
| out_2 = second.cpu().numpy() | |
| out_1 = out_1[~np.isnan(out_1)] | |
| out_2 = out_2[~np.isnan(out_2)] | |
| max_diff = np.amax(np.abs(out_1 - out_2)) | |
| self.assertLessEqual(max_diff, 1e-5) | |
| def test_attention_outputs(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| decoder_seq_length = ( | |
| self.model_tester.decoder_seq_length | |
| if hasattr(self.model_tester, "decoder_seq_length") | |
| else self.model_tester.seq_length | |
| ) | |
| encoder_seq_length = ( | |
| self.model_tester.encoder_seq_length | |
| if hasattr(self.model_tester, "encoder_seq_length") | |
| else self.model_tester.seq_length | |
| ) | |
| decoder_key_length = ( | |
| self.model_tester.key_length if hasattr(self.model_tester, "key_length") else decoder_seq_length | |
| ) | |
| encoder_key_length = ( | |
| self.model_tester.key_length if hasattr(self.model_tester, "key_length") else encoder_seq_length | |
| ) | |
| for model_class in self.all_model_classes: | |
| config.output_attentions = True | |
| config.output_hidden_states = False | |
| model = model_class(config) | |
| model.to(torch_device) | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| attentions = outputs[-1] | |
| self.assertEqual(model.config.output_attentions, True) | |
| self.assertEqual(model.config.output_hidden_states, False) | |
| self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) | |
| self.assertListEqual( | |
| list(attentions[0].shape[-3:]), | |
| [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], | |
| ) | |
| out_len = len(outputs) | |
| if self.is_encoder_decoder: | |
| self.assertEqual(out_len % 2, 0) | |
| decoder_attentions = outputs[(out_len // 2) - 1] | |
| self.assertEqual(model.config.output_attentions, True) | |
| self.assertEqual(model.config.output_hidden_states, False) | |
| self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) | |
| self.assertListEqual( | |
| list(decoder_attentions[0].shape[-3:]), | |
| [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], | |
| ) | |
| # Check attention is always last and order is fine | |
| config.output_attentions = True | |
| config.output_hidden_states = True | |
| model = model_class(config) | |
| model.to(torch_device) | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs)) | |
| self.assertEqual(model.config.output_attentions, True) | |
| self.assertEqual(model.config.output_hidden_states, True) | |
| self_attentions = outputs[-1] | |
| self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) | |
| self.assertListEqual( | |
| list(self_attentions[0].shape[-3:]), | |
| [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], | |
| ) | |
| def test_torchscript(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| self._create_and_check_torchscript(config, inputs_dict) | |
| def test_torchscript_output_attentions(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| config.output_attentions = True | |
| self._create_and_check_torchscript(config, inputs_dict) | |
| def test_torchscript_output_hidden_state(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| config.output_hidden_states = True | |
| self._create_and_check_torchscript(config, inputs_dict) | |
| def _create_and_check_torchscript(self, config, inputs_dict): | |
| if not self.test_torchscript: | |
| return | |
| configs_no_init = _config_zero_init(config) # To be sure we have no Nan | |
| configs_no_init.torchscript = True | |
| for model_class in self.all_model_classes: | |
| model = model_class(config=configs_no_init) | |
| model.to(torch_device) | |
| model.eval() | |
| inputs = inputs_dict["input_ids"] # Let's keep only input_ids | |
| try: | |
| traced_gpt2 = torch.jit.trace(model, inputs) | |
| except RuntimeError: | |
| self.fail("Couldn't trace module.") | |
| with tempfile.TemporaryDirectory() as tmp_dir_name: | |
| pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") | |
| try: | |
| torch.jit.save(traced_gpt2, pt_file_name) | |
| except Exception: | |
| self.fail("Couldn't save module.") | |
| try: | |
| loaded_model = torch.jit.load(pt_file_name) | |
| except Exception: | |
| self.fail("Couldn't load module.") | |
| model.to(torch_device) | |
| model.eval() | |
| loaded_model.to(torch_device) | |
| loaded_model.eval() | |
| model_state_dict = model.state_dict() | |
| loaded_model_state_dict = loaded_model.state_dict() | |
| self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) | |
| models_equal = True | |
| for layer_name, p1 in model_state_dict.items(): | |
| p2 = loaded_model_state_dict[layer_name] | |
| if p1.data.ne(p2.data).sum() > 0: | |
| models_equal = False | |
| self.assertTrue(models_equal) | |
| def test_headmasking(self): | |
| if not self.test_head_masking: | |
| return | |
| global_rng.seed(42) | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| global_rng.seed() | |
| config.output_attentions = True | |
| config.output_hidden_states = True | |
| configs_no_init = _config_zero_init(config) # To be sure we have no Nan | |
| for model_class in self.all_model_classes: | |
| model = model_class(config=configs_no_init) | |
| model.to(torch_device) | |
| model.eval() | |
| # Prepare head_mask | |
| # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior) | |
| head_mask = torch.ones( | |
| self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device | |
| ) | |
| head_mask[0, 0] = 0 | |
| head_mask[-1, :-1] = 0 | |
| head_mask.requires_grad_(requires_grad=True) | |
| inputs = inputs_dict.copy() | |
| inputs["head_mask"] = head_mask | |
| outputs = model(**inputs) | |
| # Test that we can get a gradient back for importance score computation | |
| output = sum(t.sum() for t in outputs[0]) | |
| output = output.sum() | |
| output.backward() | |
| multihead_outputs = head_mask.grad | |
| attentions = outputs[-1] | |
| # Remove Nan | |
| for t in attentions: | |
| self.assertLess( | |
| torch.sum(torch.isnan(t)), t.numel() / 4 | |
| ) # Check we don't have more than 25% nans (arbitrary) | |
| attentions = [ | |
| t.masked_fill(torch.isnan(t), 0.0) for t in attentions | |
| ] # remove them (the test is less complete) | |
| self.assertIsNotNone(multihead_outputs) | |
| self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers) | |
| self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0) | |
| self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0) | |
| self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0) | |
| self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0) | |
| self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0) | |
| def test_head_pruning(self): | |
| if not self.test_pruning: | |
| return | |
| for model_class in self.all_model_classes: | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| if "head_mask" in inputs_dict: | |
| del inputs_dict["head_mask"] | |
| config.output_attentions = True | |
| config.output_hidden_states = False | |
| model = model_class(config=config) | |
| model.to(torch_device) | |
| model.eval() | |
| heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]} | |
| model.prune_heads(heads_to_prune) | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| attentions = outputs[-1] | |
| self.assertEqual(attentions[0].shape[-3], 1) | |
| self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) | |
| self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) | |
| def test_head_pruning_save_load_from_pretrained(self): | |
| if not self.test_pruning: | |
| return | |
| for model_class in self.all_model_classes: | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| if "head_mask" in inputs_dict: | |
| del inputs_dict["head_mask"] | |
| config.output_attentions = True | |
| config.output_hidden_states = False | |
| model = model_class(config=config) | |
| model.to(torch_device) | |
| model.eval() | |
| heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]} | |
| model.prune_heads(heads_to_prune) | |
| with tempfile.TemporaryDirectory() as temp_dir_name: | |
| model.save_pretrained(temp_dir_name) | |
| model = model_class.from_pretrained(temp_dir_name) | |
| model.to(torch_device) | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| attentions = outputs[-1] | |
| self.assertEqual(attentions[0].shape[-3], 1) | |
| self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) | |
| self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) | |
| def test_head_pruning_save_load_from_config_init(self): | |
| if not self.test_pruning: | |
| return | |
| for model_class in self.all_model_classes: | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| if "head_mask" in inputs_dict: | |
| del inputs_dict["head_mask"] | |
| config.output_attentions = True | |
| config.output_hidden_states = False | |
| heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]} | |
| config.pruned_heads = heads_to_prune | |
| model = model_class(config=config) | |
| model.to(torch_device) | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| attentions = outputs[-1] | |
| self.assertEqual(attentions[0].shape[-3], 1) | |
| self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) | |
| self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) | |
| def test_head_pruning_integration(self): | |
| if not self.test_pruning: | |
| return | |
| for model_class in self.all_model_classes: | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| if "head_mask" in inputs_dict: | |
| del inputs_dict["head_mask"] | |
| config.output_attentions = True | |
| config.output_hidden_states = False | |
| heads_to_prune = {0: [0], 1: [1, 2]} | |
| config.pruned_heads = heads_to_prune | |
| model = model_class(config=config) | |
| model.to(torch_device) | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| attentions = outputs[-1] | |
| self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) | |
| self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) | |
| self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads) | |
| self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads) | |
| with tempfile.TemporaryDirectory() as temp_dir_name: | |
| model.save_pretrained(temp_dir_name) | |
| model = model_class.from_pretrained(temp_dir_name) | |
| model.to(torch_device) | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| attentions = outputs[-1] | |
| self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) | |
| self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) | |
| self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads) | |
| self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads) | |
| heads_to_prune = {0: [0], 2: [1, 2]} | |
| model.prune_heads(heads_to_prune) | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| attentions = outputs[-1] | |
| self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) | |
| self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2) | |
| self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2) | |
| self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads) | |
| self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]}) | |
| def test_hidden_states_output(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| for model_class in self.all_model_classes: | |
| config.output_hidden_states = True | |
| config.output_attentions = False | |
| model = model_class(config) | |
| model.to(torch_device) | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs_dict) | |
| hidden_states = outputs[-1] | |
| self.assertEqual(model.config.output_attentions, False) | |
| self.assertEqual(model.config.output_hidden_states, True) | |
| self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1) | |
| self.assertListEqual( | |
| list(hidden_states[0].shape[-2:]), | |
| [ | |
| self.model_tester.encoder_seq_length | |
| if hasattr(self.model_tester, "encoder_seq_length") | |
| else self.model_tester.seq_length, | |
| self.model_tester.hidden_size, | |
| ], | |
| ) | |
| def test_resize_tokens_embeddings(self): | |
| original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| if not self.test_resize_embeddings: | |
| return | |
| for model_class in self.all_model_classes: | |
| config = copy.deepcopy(original_config) | |
| model = model_class(config) | |
| model_vocab_size = config.vocab_size | |
| # Retrieve the embeddings and clone theme | |
| model_embed = model.resize_token_embeddings(model_vocab_size) | |
| cloned_embeddings = model_embed.weight.clone() | |
| # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size | |
| model_embed = model.resize_token_embeddings(model_vocab_size + 10) | |
| self.assertEqual(model.config.vocab_size, model_vocab_size + 10) | |
| # Check that it actually resizes the embeddings matrix | |
| self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) | |
| # Check that the model can still do a forward pass successfully (every parameter should be resized) | |
| model(**inputs_dict) | |
| # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size | |
| model_embed = model.resize_token_embeddings(model_vocab_size - 15) | |
| self.assertEqual(model.config.vocab_size, model_vocab_size - 15) | |
| # Check that it actually resizes the embeddings matrix | |
| self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) | |
| # Check that the model can still do a forward pass successfully (every parameter should be resized) | |
| # Input ids should be clamped to the maximum size of the vocabulary | |
| inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) | |
| model(**inputs_dict) | |
| # Check that adding and removing tokens has not modified the first part of the embedding matrix. | |
| models_equal = True | |
| for p1, p2 in zip(cloned_embeddings, model_embed.weight): | |
| if p1.data.ne(p2.data).sum() > 0: | |
| models_equal = False | |
| self.assertTrue(models_equal) | |
| def test_model_common_attributes(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| for model_class in self.all_model_classes: | |
| model = model_class(config) | |
| self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Embedding, AdaptiveEmbedding)) | |
| model.set_input_embeddings(torch.nn.Embedding(10, 10)) | |
| x = model.get_output_embeddings() | |
| self.assertTrue(x is None or isinstance(x, torch.nn.Linear)) | |
| def test_tie_model_weights(self): | |
| if not self.test_torchscript: | |
| return | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| def check_same_values(layer_1, layer_2): | |
| equal = True | |
| for p1, p2 in zip(layer_1.weight, layer_2.weight): | |
| if p1.data.ne(p2.data).sum() > 0: | |
| equal = False | |
| return equal | |
| for model_class in self.all_model_classes: | |
| config.torchscript = True | |
| model_not_tied = model_class(config) | |
| if model_not_tied.get_output_embeddings() is None: | |
| continue | |
| params_not_tied = list(model_not_tied.parameters()) | |
| config_tied = copy.deepcopy(config) | |
| config_tied.torchscript = False | |
| model_tied = model_class(config_tied) | |
| params_tied = list(model_tied.parameters()) | |
| # Check that the embedding layer and decoding layer are the same in size and in value | |
| self.assertGreater(len(params_not_tied), len(params_tied)) | |
| # self.assertTrue(check_same_values(embeddings, decoding)) | |
| # # Check that after modification, they remain the same. | |
| # embeddings.weight.data.div_(2) | |
| # # Check that the embedding layer and decoding layer are the same in size and in value | |
| # self.assertTrue(embeddings.weight.shape, decoding.weight.shape) | |
| # self.assertTrue(check_same_values(embeddings, decoding)) | |
| # # Check that after modification, they remain the same. | |
| # decoding.weight.data.div_(4) | |
| # # Check that the embedding layer and decoding layer are the same in size and in value | |
| # self.assertTrue(embeddings.weight.shape, decoding.weight.shape) | |
| # self.assertTrue(check_same_values(embeddings, decoding)) | |
| # Check that after resize they remain tied. | |
| model_tied.resize_token_embeddings(config.vocab_size + 10) | |
| params_tied_2 = list(model_tied.parameters()) | |
| self.assertGreater(len(params_not_tied), len(params_tied)) | |
| self.assertEqual(len(params_tied_2), len(params_tied)) | |
| # decoding.weight.data.mul_(20) | |
| # # Check that the embedding layer and decoding layer are the same in size and in value | |
| # self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape) | |
| # self.assertTrue(check_same_values(model.transformer.wte, model.lm_head)) | |
| def test_inputs_embeds(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| if not self.is_encoder_decoder: | |
| input_ids = inputs_dict["input_ids"] | |
| del inputs_dict["input_ids"] | |
| else: | |
| encoder_input_ids = inputs_dict["encoder_input_ids"] | |
| decoder_input_ids = inputs_dict["decoder_input_ids"] | |
| del inputs_dict["encoder_input_ids"] | |
| del inputs_dict["decoder_input_ids"] | |
| for model_class in self.all_model_classes: | |
| model = model_class(config) | |
| model.to(torch_device) | |
| model.eval() | |
| wte = model.get_input_embeddings() | |
| if not self.is_encoder_decoder: | |
| inputs_dict["inputs_embeds"] = wte(input_ids) | |
| else: | |
| inputs_dict["encoder_inputs_embeds"] = wte(encoder_input_ids) | |
| inputs_dict["decoder_inputs_embeds"] = wte(decoder_input_ids) | |
| with torch.no_grad(): | |
| model(**inputs_dict) | |
| global_rng = random.Random() | |
| def ids_tensor(shape, vocab_size, rng=None, name=None): | |
| """Creates a random int32 tensor of the shape within the vocab size.""" | |
| if rng is None: | |
| rng = global_rng | |
| total_dims = 1 | |
| for dim in shape: | |
| total_dims *= dim | |
| values = [] | |
| for _ in range(total_dims): | |
| values.append(rng.randint(0, vocab_size - 1)) | |
| return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous() | |
| def floats_tensor(shape, scale=1.0, rng=None, name=None): | |
| """Creates a random float32 tensor of the shape within the vocab size.""" | |
| if rng is None: | |
| rng = global_rng | |
| total_dims = 1 | |
| for dim in shape: | |
| total_dims *= dim | |
| values = [] | |
| for _ in range(total_dims): | |
| values.append(rng.random() * scale) | |
| return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous() | |
| class ModelUtilsTest(unittest.TestCase): | |
| def test_model_from_pretrained(self): | |
| logging.basicConfig(level=logging.INFO) | |
| for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: | |
| config = BertConfig.from_pretrained(model_name) | |
| self.assertIsNotNone(config) | |
| self.assertIsInstance(config, PretrainedConfig) | |
| model = BertModel.from_pretrained(model_name) | |
| model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True) | |
| self.assertIsNotNone(model) | |
| self.assertIsInstance(model, PreTrainedModel) | |
| for value in loading_info.values(): | |
| self.assertEqual(len(value), 0) | |
| config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) | |
| model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) | |
| self.assertEqual(model.config.output_attentions, True) | |
| self.assertEqual(model.config.output_hidden_states, True) | |
| self.assertEqual(model.config, config) | |